High definition map and route storage management system for autonomous vehicles

ABSTRACT

High definition maps for autonomous vehicles are very high resolution and detailed, and hence require storage of a great deal of data. A vehicle computing system provides multi-layered caching makes this data usable in a system that requires very low latency on every operation. The system determines which routes are most likely to be driven in the near future by the car, and ensures that the route is cached on the vehicle before beginning the route. The system provides efficient formats for moving map data from server to car and for managing the on-car disk. The system further provides real-time accessibility of nearby map data as the car moves, while providing data access at optimal speeds.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. application Ser.No. 16/786,835 filed on Feb. 10, 2020, which is a continuation of Ser.No. 16/408,270 filed on May 9, 2019, which claims benefit of priority toU.S. application Ser. No. 15/857,558 filed on Dec. 28, 2017, and U.S.Provisional Application No. 62/441,072, filed on Dec. 30, 2016, all ofwhich are incorporated by reference.

BACKGROUND

This disclosure relates generally to maps for autonomous vehicles, andmore particularly to providing high definition maps with high precisionand up-to-date map data to autonomous vehicles for safe navigation.

Autonomous vehicles, also known as self-driving cars, driverless cars,auto, or robotic cars, drive from a source location to a destinationlocation without requiring a human driver to control and navigate thevehicle. Automation of driving is difficult due to several reasons. Forexample, autonomous vehicles use sensors to make driving decisions onthe fly, but vehicle sensors cannot observe everything all the time.Vehicle sensors can be obscured by corners, rolling hills, and othervehicles. Vehicles sensors may not observe certain things early enoughto make decisions. In addition, lanes and signs may be missing on theroad or knocked over or hidden by bushes, and therefore not detectableby sensors. Furthermore, road signs for rights of way may not be readilyvisible for determining from where vehicles could be coming, or forswerving or moving out of a lane in an emergency or when there is astopped obstacle that must be passed.

Autonomous vehicles can use map data to figure out some of the aboveinformation instead of relying on sensor data. However conventional mapshave several drawbacks that make them difficult to use for an autonomousvehicle. For example maps do not provide the level of accuracy requiredfor safe navigation (e.g., 10 cm or less). GPS systems provideaccuracies of approximately 3-5 meters, but have large error conditionsresulting in an accuracy of over 100 m. This makes it challenging toaccurately determine the location of the vehicle.

Furthermore, conventional maps are created by survey teams that usedrivers with specially outfitted cars with high resolution sensors thatdrive around a geographic region and take measurements. The measurementsare taken back and a team of map editors assembles the map from themeasurements. This process is expensive and time consuming (e.g., takingpossibly months to complete a map). Therefore, maps assembled using suchtechniques do not have fresh data. For example, roads areupdated/modified on a frequent basis roughly 5-10% per year. But surveycars are expensive and limited in number, so cannot capture most ofthese updates. For example, a survey fleet may include a thousand cars.For even a single state in the United States, a thousand cars would notbe able to keep the map up-to-date on a regular basis to allow safeself-driving. As a result, conventional techniques of maintaining mapsare unable to provide the right data that is sufficiently accurate andup-to-date for safe navigation of autonomous vehicles. Additionallythere is a tradeoff in higher resolution maps in that they are larger interms of size of data, making storage of these high resolution maps inautonomous vehicles challenging. With constant updating and modifying ofthe high resolution maps, transmitting an entire high resolution map toeach autonomous vehicle is costly in computing resources and timerequired for transmission.

SUMMARY

Embodiments of the invention generate and maintain high definition (HD)maps that are detailed, very high resolution (e.g. 5 cm resolution), andprovide the most updated road conditions for safe navigation. Thisrequires a lot of data, such as multiple Petabytes to cover one country.Embodiments of the invention describe efficient methods of transferringHD maps between an online system and an autonomous vehicle computingsystem, and the data can be made usable in a system that requires verylow latency on every operation. For example, the caching system givesthe appearance of immediate and efficient access of a large (e.g.,multi-Petabyte) database to a processor controlling a vehicle with onlya much smaller amount of storage (e.g., a few hundred Gigabyte disk andseveral Gigabytes of random-access memory (RAM)). The system provides(1) efficient (in time, space, bandwidth etc.) formats for moving mapdata between an online system (e.g., on the cloud) to a vehicle, (2)efficient management of the on-vehicle disk, (3) real-time accessibilityof the relevant (nearby) map data as the vehicle moves through itsenvironment, and (4) access to all data as if it were already in RAM andaccessible at optimal speed.

In some embodiments, the system provides this efficient method oftransferring HD maps by partitioning the maps into map tiles that arethen compressed prior to being transferred. The method generallycomprises receiving these compressed map tiles corresponding to a routeof an autonomous vehicle from the online system, and decompressing maptiles. The autonomous vehicle can further determine its geographicallocation for determining which decompressed map tiles to upload into itsRAM in preparation for use by a route manager by the autonomous vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows the overall system environment of an HD map systeminteracting with multiple vehicle computing systems, according to anembodiment.

FIG. 2 shows the system architecture of a vehicle computing system,according to an embodiment.

FIG. 3 illustrates the various layers of instructions in the HD Map APIof a vehicle computing system, according to an embodiment.

FIG. 4 shows the system architecture of an online HD map system,according to an embodiment.

FIG. 5 illustrates the components of an HD map, according to anembodiment.

FIGS. 6A-B illustrate geographical regions defined in an HD map,according to an embodiment.

FIG. 7 illustrates representations of lanes in an HD map, according toan embodiment.

FIGS. 8A-B illustrates lane elements and relations between lane elementsin an HD map, according to an embodiment.

FIG. 9 shows the system architecture of the HD map caching manager,according to an embodiment.

FIG. 10 illustrates a method of efficiently utilizing compressed maptiles of the HD map from the online HD map system by a vehicle computingsystem, according to an embodiment.

FIG. 11 illustrates an example of the method of efficiently transferringmap tiles of the HD map, according to an embodiment.

FIG. 12 illustrates a flowchart of the method of efficientlytransferring map tiles of the HD map, according to an embodiment.

FIG. 13 illustrates an example of a process of loading accessible maptiles in a random- access memory (RAM) for use in driving the vehicle,according to an embodiment.

FIG. 14 illustrates an embodiment of a computing machine that can readinstructions from a machine-readable medium and execute the instructionsin a processor or controller.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION

Overview

Embodiments of the invention maintain high definition (HD) mapscontaining up to date information using high precision. The HD maps maybe used by autonomous vehicles to safely navigate to their destinationswithout human input or with limited human input. An autonomous vehicleis a vehicle capable of sensing its environment and navigating withouthuman input. Autonomous vehicles may also be referred to herein as“driverless car,” “self-driving car,” or “robotic car.” An HD map refersto a map storing data with very high precision, typically 5-10 cm.Embodiments generate HD maps containing spatial geometric informationabout the roads on which an autonomous vehicle can travel. Accordingly,the generated HD maps include the information necessary for anautonomous vehicle navigating safely without human intervention. Insteadof collecting data for the HD maps using an expensive and time consumingmapping fleet process including vehicles outfitted with high resolutionsensors, embodiments of the invention use data from the lower resolutionsensors of the self-driving vehicles themselves as they drive aroundthrough their environments. The vehicles may have no prior map data forthese routes or even for the region. Embodiments of the inventionprovide location as a service (LaaS) such that autonomous vehicles ofdifferent manufacturers can each have access to the most up-to-date mapinformation created via these embodiments of invention.

Embodiments generate and maintain high definition (HD) maps that areaccurate and include the most updated road conditions for safenavigation. For example, the HD maps provide the current location of theautonomous vehicle relative to the lanes of the road precisely enough toallow the autonomous vehicle to drive safely in the lane. HD maps storea very large amount of information, and therefore face challenges inmanaging the information. For example, an HD map for a large geographicregion may not fit on the local storage of a vehicle. Embodiments of theinvention provide the necessary portion of an HD map to an autonomousvehicle that allows the vehicle to determine its current location in theHD map, determine the features on the road relative to the vehicle'sposition, determine if it is safe to move the vehicle based on physicalconstraints and legal constraints, etc. Examples of physical constraintsinclude physical obstacles, such as walls, and examples of legalconstraints include legally allowed direction of travel for a lane,speed limits, yields, stops.

Since it is not possible to download an entire map onto a car, thesystem manages what is kept on the car in cache. The system providesroute management to determine which routes are most likely to be drivenin the near future by the car. When a route is selected to be driven,the system ensures that the data for that route is cached on the carbefore the car begins driving the route. Once the data is downloadedonto the car, the system makes sure the car processing system can usethe data efficiently. To use the data, it typically needs to be loadedinto memory. To use the data efficiently (low latency), it typicallyneeds to be accessible using some optimized data structure (e.g., anindexed data structure like a map or a tree). The index may be designedaround the primary access patterns including: (1) lookup by ID: a hashmap or (2) lookup by spatial location: a tree, such as KD-tree or R-treeor spatial hash map. The system could send down indexed data from thecloud but this would add a lot of data to an already slow/costly networkconnection. Instead, the system can optimize the network bandwidth usageby sending a very highly compressed form of the map data that containsonly the bare necessities for the current routing the car will perform(e.g., no indexes). This provides a relatively small payload from thecloud and a small disk footprint when stored on the car's disk. Thedifferent levels of caching in this multi-layered caching system aredescribed in more detail below.

Embodiments of the invention allow safe navigation for an autonomousvehicle by providing high latency, for example, 10-20 milliseconds orless for providing a response to a request; high accuracy in terms oflocation, i.e., accuracy within 10 cm or less; freshness of data byensuring that the map is updated to reflect changes on the road within areasonable time frame; and storage efficiency by minimizing the storageneeded for the HD Map.

FIG. 1 shows the overall system environment of an HD map systeminteracting with multiple vehicles, according to an embodiment. The HDmap system 100 includes an online HD map system 110 that interacts witha plurality of vehicles 150. The vehicles 150 may be autonomous vehiclesbut are not required to be. The online HD map system 110 receives sensordata captured by sensors of the vehicles, and combines the data receivedfrom the vehicles 150 to generate and maintain HD maps. The online HDmap system 110 sends HD map data to the vehicles for use in driving thevehicles. In an embodiment, the online HD map system 110 is implementedas a distributed computing system, for example, a cloud based servicethat allows clients such as vehicle computing systems 120 to makerequests for information and services. For example, a vehicle computingsystem 120 may make a request for HD map data for driving along a routeand the online HD map system 110 provides the requested HD map data.

FIG. 1 and the other figures use like reference numerals to identifylike elements. A letter after a reference numeral, such as “105A,”indicates that the text refers specifically to the element having thatparticular reference numeral. A reference numeral in the text without afollowing letter, such as “105,” refers to any or all of the elements inthe figures bearing that reference numeral (e.g. “105” in the textrefers to reference numerals “105A” and/or “105N” in the figures).

The online HD map system 110 comprises a vehicle interface module 160and an HD map store 165. The online HD map system 110 interacts with thevehicle computing system 120 of various vehicles 150 using the vehicleinterface module 160. The online HD map system 110 stores mapinformation for various geographical regions in the HD map store 165.The online HD map system 110 may include other modules than those shownin FIG. 1 , for example, various other modules as illustrated in FIG. 4and further described herein.

The online HD map system 110 receives 115 data collected by sensors of aplurality of vehicles 150, for example, hundreds or thousands of cars.The vehicles provide sensor data captured while driving along variousroutes and send it to the online HD map system 110. The online HD mapsystem 110 uses the data received from the vehicles 150 to create andupdate HD maps describing the regions in which the vehicles 150 aredriving. The online HD map system 110 builds high definition maps basedon the collective information received from the vehicles 150 and storesthe HD map information in the HD map store 165.

The online HD map system 110 sends 125 HD maps to individual vehicles150 as required by the vehicles 150. For example, if an autonomousvehicle needs to drive along a route, the vehicle computing system 120of the autonomous vehicle provides information describing the routebeing travelled to the online HD map system 110. In response, the onlineHD map system 110 provides the required HD maps for driving along theroute. As will be further described, methods of efficiently utilizingthe HD maps by the autonomous vehicles implement varying formats ofsections of the HD maps.

In an embodiment, the online HD map system 110 sends portions of the HDmap data to the vehicles in a compressed format so that the datatransmitted consumes less bandwidth. The online HD map system 110receives from various vehicles, information describing the data that isstored at the local HD map store 275 of the vehicle. If the online HDmap system 110 determines that the vehicle does not have certain portionof the HD map stored locally in the local HD map store 275, the onlineHD map system 110 sends that portion of the HD map to the vehicle. Ifthe online HD map system 110 determines that the vehicle did previouslyreceive that particular portion of the HD map but the corresponding datawas updated by the online HD map system 110 since the vehicle lastreceived the data, the online HD map system 110 sends an update for thatportion of the HD map stored at the vehicle. This allows the online HDmap system 110 to minimize the amount of data that is communicated withthe vehicle and also to keep the HD map data stored locally in thevehicle updated on a regular basis.

A vehicle 150 includes vehicle sensors 105, vehicle controls 130, and avehicle computing system 120. The vehicle sensors 105 allow the vehicle150 to detect the surroundings of the vehicle as well as informationdescribing the current state of the vehicle, for example, informationdescribing the location and motion parameters of the vehicle. Thevehicle sensors 105 comprise a camera, a light detection and rangingsensor (LIDAR), a global positioning system (GPS) navigation system, aninertial measurement unit (IMU), and others. The vehicle has one or morecameras that capture images of the surroundings of the vehicle. A LIDARsurveys the surroundings of the vehicle by measuring distance to atarget by illuminating that target with a laser light pulses, andmeasuring the reflected pulses. The GPS navigation system determines theposition of the vehicle based on signals from satellites. An IMU is anelectronic device that measures and reports motion data of the vehiclesuch as velocity, acceleration, direction of movement, speed, angularrate, and so on using a combination of accelerometers and gyroscopes orother measuring instruments.

The vehicle controls 130 control the physical movement of the vehicle,for example, acceleration, direction change, starting, stopping, and soon. The vehicle controls 130 include the machinery for controlling theaccelerator, brakes, steering wheel, and so on. The vehicle computingsystem 120 continuously provides control signals to the vehicle controls130, thereby causing an autonomous vehicle to drive along a selectedroute.

The vehicle computing system 120 performs various tasks includingprocessing data collected by the sensors as well as map data receivedfrom the online HD map system 110. In some embodiments, the vehiclecomputing system 120 converts map data received from the online HD mapsystem 110 into various formats for ease of use in navigating thevehicle 150. The vehicle computing system 120 also processes data forsending to the online HD map system 110. Details of the vehiclecomputing system are illustrated in FIG. 2 and further described inconnection with FIG. 2 .

The interactions between the vehicle computing systems 120 and theonline HD map system 110 are typically performed via a network, forexample, via the Internet. The network enables communications betweenthe vehicle computing systems 120 and the online HD map system 110. Inone embodiment, the network uses standard communications technologiesand/or protocols. The data exchanged over the network can be representedusing technologies and/or formats including the hypertext markuplanguage (HTML), the extensible markup language (XML), etc. In addition,all or some of links can be encrypted using conventional encryptiontechnologies such as secure sockets layer (SSL), transport layersecurity (TLS), virtual private networks (VPNs), Internet Protocolsecurity (IPsec), etc. In another embodiment, the entities can usecustom and/or dedicated data communications technologies instead of, orin addition to, the ones described above.

FIG. 2 shows the system architecture of a vehicle computing system,according to an embodiment. The vehicle computing system 120 comprises aperception module 210, prediction module 215, planning module 220, acontrol module 225, a local HD map store 275, an HD map system interface280, a route generation module 290, a HD map caching manager 295, and anHD map application programming interface (API) 205. The various modulesof the vehicle computing system 120 process various type of dataincluding sensor data 230, a behavior model 235, routes 240, HD mapdata, and physical constraints 245. In other embodiments, the vehiclecomputing system 120 may have more or fewer modules. Functionalitydescribed as being implemented by a particular module may be implementedby other modules.

The perception module 210 receives sensor data 230 from the sensors 105of the vehicle 150. This includes data collected by cameras of the car,LIDAR, IMU, GPS navigation system, and so on. The perception module 210uses the sensor data to determine what objects are around the vehicle,the details of the road on which the vehicle is travelling, and so on.The perception module 210 processes the sensor data 230 to populate datastructures storing the sensor data and provides the information to theprediction module 215.

The prediction module 215 interprets the data provided by the perceptionmodule using behavior models of the objects perceived to determinewhether an object is moving or likely to move. For example, theprediction module 215 may determine that objects representing road signsare not likely to move, whereas objects identified as vehicles, people,and so on, are either moving or likely to move. The prediction module215 uses the behavior models 235 of various types of objects todetermine whether they are likely to move. The prediction module 215provides the predictions of various objects to the planning module 200to plan the subsequent actions that the vehicle needs to take next.Predictions by the prediction module 215 can influence routes by thevehicle 150 and correspondingly which maps to access for safenavigation.

The planning module 200 receives the information describing thesurroundings of the vehicle from the prediction module 215, the route240 that determines the destination of the vehicle, and the path thatthe vehicle should take to get to the destination. The planning module200 uses the information from the prediction module 215 and the route240 to plan a sequence of actions that the vehicle needs to take withina short time interval, for example, within the next few seconds. In anembodiment, the planning module 200 specifies the sequence of actions asone or more points representing nearby locations that the vehicle needsto drive through next. The planning module 200 provides the details ofthe plan comprising the sequence of actions to be taken by the vehicleto the control module 225. The plan may determine the subsequent actionof the vehicle, for example, whether the vehicle performs a lane change,a turn, acceleration by increasing the speed or slowing down, and so on.Additionally, the planning module 200 may prompt processing or accessingof various sections of the HD map in preparation vehicle actions.

The control module 225 determines the control signals for sending to thecontrols 130 of the vehicle based on the plan received from the planningmodule 200. For example, if the vehicle is currently at point A and theplan specifies that the vehicle should next go to a nearby point B, thecontrol module 225 determines the control signals for the controls 130that would cause the vehicle to go from point A to point B in a safe andsmooth way, for example, without taking any sharp turns or a zig zagpath from point A to point B. The path taken by the vehicle to go frompoint A to point B may depend on the current speed and direction of thevehicle as well as the location of point B with respect to point A. Forexample, if the current speed of the vehicle is high, the vehicle maytake a wider turn compared to a vehicle driving slowly. In someinstances, the control module 225 determines the control signals basedon accessing the HD map.

The control module 225 also receives physical constraints 245 as input.These include the physical capabilities of that specific vehicle. Forexample, a car having a particular make and model may be able to safelymake certain types of vehicle movements such as acceleration, and turnsthat another car with a different make and model may not be able to makesafely. The control module 225 incorporates these physical constraintsin determining the control signals. The control module 225 sends thecontrol signals to the vehicle controls 130 that cause the vehicle toexecute the specified sequence of actions causing the vehicle to move asplanned. The above steps are constantly repeated every few secondscausing the vehicle to drive safely along the route that was planned forthe vehicle.

The route generation module 290 computes and determines the optimalroute traversing from a source address (or source location) to adestination address (or destination location). The route generationmodule 290 stores a set of partial routes and grows them to obtain afinal route. In some embodiments, the route generation module 290 maydynamically adjust the route as the vehicle 120 drives along the route.Accordingly, the route generation module 290 may query another modulefor updating or preparing map data for use by the control module 225. Insome instances, the route generation module 290 queries the HD mapcaching manager 295 for updating or preparing map data for use. Somefunctionality of the route generation module 260 may be performed in theonline HD map system 110. Accordingly, the online HD map system 110 maystore a corresponding route generation module 260 that interacts withthe route generation module 260 stored in the vehicle computing system120.

The HD map caching manager 295 processes the HD map data as receivedfrom the online HD map system 110. After processing the HD map data, theHD map caching manager 295 stores the HD map data in the local HD mapstore 275. Processing the HD map data by the HD map caching manager 295may involve a variety of processes so as to provide the HD map data foruse by the various modules of the vehicle computing system 120 withoptimal efficiency. Processing the HD map data may include, but notlimited to, decompressing compressed formats of the HD map data,indexing the HD map data, partitioning the HD map data into subsections,loading the HD map data, and storing the HD map data. Additionally, theHD map caching manager 295 may comprise a variety of modules foraccomplishing the processing of the HD map data and/or a variety ofstores for storing the HD map data in its initial format, itsintermediary formats in processing of the HD map data, and/or itsterminal format for use by the other modules. These processes will befurther described in detail in conjunction with FIG. 9-12 .

The various modules of the vehicle computing system 120 including theperception module 210, prediction module 215, and planning module 220receive map information to perform their respective computation. The HDmap caching manager 295 manages and stores the HD map data in the localHD map store 275. The modules of the vehicle computing system 120interact with the map data using the HD map API 205 that provides a setof application programming interfaces (APIs) that can be invoked by amodule for accessing the map information. The HD map system interface280 allows the vehicle computing system 120 to interact with the onlineHD map system 110 via a network (not shown in the Figures). The local HDmap store 275 stores map data in a format specified by the HD mapcaching manager 295. The HD map API 205 is capable of processing the HDmap data format for use by the various modules of the vehicle computingsystem 120. The HD map API 205 provides the vehicle computing system 120with an interface for interacting with the HD map data. The HD map API205 includes several APIs including the localization API 250, thelandmark map API 255, the route API 265, the 3D map API 270, the mapupdate API 285, and so on. In other embodiments, the HD map cachingmanager 295 stores the HD map data in various stores with the HD mapcaching manager 295, as will be described in conjunction with FIG. 9-12. In these embodiments, the HD map caching manager 295 interacts the HDmap API 205 for providing an interface for utilizing the HD map data.

The localization APIs 250 determine the current location of the vehicle,for example, when the vehicle starts and as the vehicle moves along aroute. The localization APIs 250 include a localize API that determinesan accurate location of the vehicle within the HD Map. The vehiclecomputing system 120 can use the location as an accurate relativepositioning for making other queries, for example, feature queries,navigable space queries, and occupancy map queries further describedherein. The localize API receives inputs comprising one or more of,location provided by GPS, vehicle motion data provided by IMU, LIDARscanner data, and camera images. The localize API returns an accuratelocation of the vehicle as latitude and longitude coordinates. Thecoordinates returned by the localize API are more accurate compared tothe GPS coordinates used as input, for example, the output of thelocalize API may have precision range from 5-10 cm. In one embodiment,the vehicle computing system 120 invokes the localization API 250 todetermine location of the vehicle periodically based on the LIDAR usingscanner data, for example, at a frequency of 10 Hz. The vehiclecomputing system 120 may invoke the localization API 250 to determinethe vehicle location at a higher rate (e.g., 60 Hz) if GPS/IMU data isavailable at that rate. The vehicle computing system 120 stores asinternal state, location history records to improve accuracy ofsubsequent localize calls. The location history record stores history oflocation from the point-in-time, when the car was turned off/stopped.The localization API 250 includes a localize-route API generates anaccurate route specifying lanes based on the HD map. The localize-routeAPI takes as input a route from a source to destination via a thirdparty maps and generates a high precision routes represented as aconnected graph of navigable lanes along the input routes based on HDmaps.

The landmark map API 255 provides the geometric and semantic descriptionof the world around the vehicle, for example, description of variousportions of lanes that the vehicle is currently travelling on. Thelandmark map APIs 255 comprise APIs that allow queries based on landmarkmaps, for example, fetch-lanes API and fetch-features API. Thefetch-lanes API provide lane information relative to the vehicle and thefetch-features API. The fetch-lanes API receives as input a location,for example, the location of the vehicle specified using latitude andlongitude of the vehicle and returns lane information relative to theinput location. The fetch-lanes API may specify a distance parametersindicating the distance relative to the input location for which thelane information is retrieved. The fetch-features API receivesinformation identifying one or more lane elements and returns landmarkfeatures relative to the specified lane elements. The landmark featuresinclude, for each landmark, a spatial description that is specific tothe type of landmark.

The 3D map API 265 provides efficient access to the spatial3-dimensional (3D) representation of the road and various physicalobjects around the road as stored in the local HD map store 275. The 3Dmap APIs 365 include a fetch-navigable-surfaces API and afetch-occupancy-grid API. The fetch-navigable-surfaces API receives asinput, identifiers for one or more lane elements and returns navigableboundaries for the specified lane elements. The fetch-occupancy-grid APIreceives a location as input, for example, a latitude and longitude ofthe vehicle, and returns information describing occupancy for thesurface of the road and all objects available in the HD map near thelocation. The information describing occupancy includes a hierarchicalvolumetric grid of all positions considered occupied in the map. Theoccupancy grid includes information at a high resolution near thenavigable areas, for example, at curbs and bumps, and relatively lowresolution in less significant areas, for example, trees and wallsbeyond a curb. The fetch-occupancy-grid API is useful for detectingobstacles and for changing direction if necessary.

The 3D map API 265 can manage accessing of the 3D maps from the local HDmap store 275 for use by the vehicle computing system 120. The 3D mapAPI 265 retrieves portions of 3D maps from the local HD map store 275;in some embodiments, portions of the 3D map is in a compressed format.In these embodiments, the 3D map API 265 decompresses portions of the 3Dmap relevant to a current route. The decompressed portions of the 3D mapare then selectively loaded onto a random-access memory (RAM) foraccessible use by other components of the vehicle computing system 120through the HD map API 205. Detailed description of methods of managingaccess of the 3D maps will be discussed in conjunction with FIG. 9 .

The 3D map API 265 also include map update APIs, for example,download-map-updates API and upload-map-updates API. Thedownload-map-updates API receives as input a planned route identifierand downloads map updates for data relevant to all planned routes or fora specific planned route. The upload-map-updates API uploads datacollected by the vehicle computing system 120 to the online HD mapsystem 110. This allows the online HD map system 110 to keep the HD mapdata stored in the online HD map system 110 up to date based on changesin map data observed by sensors of vehicles driving along variousroutes. In some instances, the download-map-updates API is prompted bythe HD map caching manager 295 when HD map data needs to be updated.

The route API 270 returns route information including full route betweena source and destination and portions of route as the vehicle travelsalong the route. The 3D map API 365 allows querying the HD Map. Theroute API 270 include add-planned-routes API and get-planned-route API.The add-planned-routes API provides information describing plannedroutes to the online HD map system 110 so that information describingrelevant HD maps can be downloaded by the vehicle computing system 120and kept up to date. The add-planned-routes API receives as input, aroute specified using polylines expressed in terms of latitudes andlongitudes and also a time-to-live (TTL) parameter specifying a timeperiod after which the route data can be deleted. Accordingly, theadd-planned-routes API allows the vehicle to indicate the route thevehicle is planning on taking in the near future as an autonomous trip.The add-planned-route API aligns the route to the HD map, records theroute and its TTL value, and makes sure that the HD map data for theroute stored in the vehicle computing system 120 is up to date. Theget-planned-routes API returns a list of planned routes and providesinformation describing a route identified by a route identifier.

The map update API 285 manages operations related to update of map data,both for the local HD map store 275 and for the HD map store 165 storedin the online HD map system 110. Accordingly, modules in the vehiclecomputing system 120 invoke the map update API 285 for downloading datafrom the online HD map system 110 to the vehicle computing system 120for storing in the local HD map store 275 as necessary. The map updateAPI 285 also allows the vehicle computing system 120 to determinewhether the information monitored by the vehicle sensors 105 indicates adiscrepancy in the map information provided by the online HD map system110 and uploads data to the online HD map system 110 that may result inthe online HD map system 110 updating the map data stored in the HD mapstore 165 that is provided to other vehicles 150.

FIG. 4 illustrates the various layers of instructions in the HD Map APIof a vehicle computing system, according to an embodiment. Differentmanufacturer of vehicles have different instructions for receivinginformation from vehicle sensors 105 and for controlling the vehiclecontrols 130. Furthermore, different vendors provide different computerplatforms with autonomous driving capabilities, for example, collectionand analysis of vehicle sensor data. Examples of computer platform forautonomous vehicles include platforms provided vendors, such as NVIDIA,QUALCOMM, and INTEL. These platforms provide functionality for use byautonomous vehicle manufacturers in manufacture of autonomous vehicles.A vehicle manufacturer can use any one or several computer platforms forautonomous vehicles. The online HD map system 110 provides a library forprocessing HD maps based on instructions specific to the manufacturer ofthe vehicle and instructions specific to a vendor specific platform ofthe vehicle. The library provides access to the HD map data and allowsthe vehicle to interact with the online HD map system 110.

As shown in FIG. 3 , in an embodiment, the HD map API is implemented asa library that includes a vehicle manufacturer adapter 310, a computerplatform adapter 320, and a common HD map API layer 330. The common HDmap API layer comprises generic instructions that can be used across aplurality of vehicle computer platforms and vehicle manufacturers. Thecomputer platform adapter 320 include instructions that are specific toeach computer platform. For example, the common HD Map API layer 330 mayinvoke the computer platform adapter 320 to receive data from sensorssupported by a specific computer platform. The vehicle manufactureradapter 310 comprises instructions specific to a vehicle manufacturer.For example, the common HD map API layer 330 may invoke functionalityprovided by the vehicle manufacturer adapter 310 to send specificcontrol instructions to the vehicle controls 130.

The online HD map system 110 stores computer platform adapters 320 for aplurality of computer platforms and vehicle manufacturer adapters 310for a plurality of vehicle manufacturers. The online HD map system 110determines the particular vehicle manufacturer and the particularcomputer platform for a specific autonomous vehicle. The online HD mapsystem 110 selects the vehicle manufacturer adapter 310 for theparticular vehicle manufacturer and the computer platform adapter 320the particular computer platform of that specific vehicle. The online HDmap system 110 sends instructions of the selected vehicle manufactureradapter 310 and the selected computer platform adapter 320 to thevehicle computing system 120 of that specific autonomous vehicle. Thevehicle computing system 120 of that specific autonomous vehicleinstalls the received vehicle manufacturer adapter 310 and the computerplatform adapter 320. The vehicle computing system 120 periodicallychecks if the online HD map system 110 has an update to the installedvehicle manufacturer adapter 310 and the computer platform adapter 320.If a more recent update is available compared to the version installedon the vehicle, the vehicle computing system 120 requests and receivesthe latest update and installs it.

HD Map System Architecture

FIG. 4 shows the system architecture of an HD map system, according toan embodiment. The online HD map system 110 comprises a map creationmodule 410, a map update module 420, a map data encoding module 430, aload balancing module 440, a map accuracy management module, a vehicleinterface module, and a HD map store 165. Other embodiments of online HDmap system 110 may include more or fewer modules than shown in FIG. 4 .Functionality indicated as being performed by a particular module may beimplemented by other modules. In an embodiment, the online HD map system110 may be a distributed system comprising a plurality of processors.

The map creation module 410 creates the map from map data collected fromseveral vehicles that are driving along various routes. The map updatemodule 420 updates previously computed map data by receiving more recentinformation from vehicles that recently travelled along routes on whichmap information changed. For example, if certain road signs have changedor lane information has changed as a result of construction in a region,the map update module 420 updates the maps accordingly. The map dataencoding module 430 encodes map data to be able to store the map dataefficiently as well as send the required map data to vehicles 150efficiently. To accomplish this, the map data encoding module 430 cancompress the map data prior to sending over the required map data tovehicles 150. The load balancing module 440 balances load acrossvehicles to ensure that requests to receive data from vehicles areuniformly distributed across different vehicles. The map accuracymanagement module 450 maintains high accuracy of the map data usingvarious techniques even though the information received from individualvehicles may not have high accuracy.

FIG. 5 illustrates the components of an HD map, according to anembodiment. The HD map comprises maps of several geographical regions.The HD map 510 of a geographical region comprises a landmark map (LMap)520 and an occupancy map (OMap) 530. The landmark map comprisesinformation describing lanes including spatial location of lanes andsemantic information about each lane. The spatial location of a lanecomprises the geometric location in latitude, longitude and elevation athigh prevision, for example, at or below 10 cm precision. The semanticinformation of a lane comprises restrictions such as direction, speed,type of lane (for example, a lane for going straight, a left turn lane,a right turn lane, an exit lane, and the like), restriction on crossingto the left, connectivity to other lanes and so on. The landmark map mayfurther comprise information describing stop lines, yield lines, spatiallocation of cross walks, safely navigable space, spatial location ofspeed bumps, curb, and road signs comprising spatial location and typeof all signage that is relevant to driving restrictions. Examples ofroad signs described in an HD map include stop signs, traffic lights,speed limits, one-way, do-not-enter, yield (vehicle, pedestrian,animal), and so on.

The occupancy map 530 comprises spatial 3-dimensional (3D)representation of the road and all physical objects around the road. Thedata stored in an occupancy map 530 is also referred to herein asoccupancy grid data. The 3D representation may be associated with aconfidence score indicative of a likelihood of the object existing atthe location. The occupancy map 530 may be represented in a number ofother ways. In one embodiment, the occupancy map 530 is represented as a3D mesh geometry (collection of triangles) which covers the surfaces. Inanother embodiment, the occupancy map 530 is represented as a collectionof 3D points which cover the surfaces. In another embodiment, theoccupancy map 530 is represented using a 3D volumetric grid of cells at5-10 cm resolution. Each cell indicates whether or not a surface existsat that cell, and if the surface exists, a direction along which thesurface is oriented.

The occupancy map 530 may take a large amount of storage space comparedto a landmark map 520. For example, data of 1 GB/Mile may be used by anoccupancy map 530, resulting in the map of the United States (including4 million miles of road) occupying 4×1015 bytes or 4 petabytes.Therefore the online HD map system 110 and the vehicle computing system120 use data compression techniques for being able to store and transfermap data thereby reducing storage and transmission costs. Accordingly,the techniques disclosed herein make self-driving of autonomous vehiclespossible.

In one embodiment, the HD Map does not require or rely on data typicallyincluded in maps, such as addresses, road names, ability to geo-code anaddress, and ability to compute routes between place names or addresses.The vehicle computing system 120 or the online HD map system 110accesses other map systems, for example, GOOGLE MAPs to obtain thisinformation. Accordingly, a vehicle computing system 120 or the onlineHD map system 110 receives navigation instructions from a tool such asGOOGLE MAPs into a route and converts the information to a route basedon the HD map information.

Geographical Regions in HD Maps

The online HD map system 110 divides a large physical area intogeographical regions and stores a representation of each geographicalregion. Each geographical region represents a contiguous area bounded bya geometric shape, for example, a rectangle or square. In an embodiment,the online HD map system 110 divides a physical area into geographicalregions of the same size independent of the amount of data required tostore the representation of each geographical region. In anotherembodiment, the online HD map system 110 divides a physical area intogeographical regions of different sizes, where the size of eachgeographical region is determined based on the amount of informationneeded for representing the geographical region. For example, ageographical region representing a densely populated area with a largenumber of streets represents a smaller physical area compared to ageographical region representing sparsely populated area with very fewstreets. Accordingly, in this embodiment, the online HD map system 110determines the size of a geographical region based on an estimate of anamount of information required to store the various elements of thephysical area relevant for an HD map.

In an embodiment, the online HD map system 110 represents a geographicregion using an object or a data record that comprises variousattributes including, a unique identifier for the geographical region, aunique name for the geographical region, description of the boundary ofthe geographical region, for example, using a bounding box of latitudeand longitude coordinates, and a collection of landmark features andoccupancy grid data.

FIGS. 6A-B illustrate geographical regions defined in an HD map,according to an embodiment. FIG. 6A shows a square geographical region610 a. FIG. 6B shows two neighboring geographical regions 610 a and 610b. The online HD map system 110 stores data in a representation of ageographical region that allows for smooth transition from onegeographical region to another as a vehicle drives across geographicalregion boundaries.

According to an embodiment, as illustrated in FIG. 6 , each geographicregion has a buffer of a predetermined width around it. The buffercomprises redundant map data around all 4 sides of a geographic region(in the case that the geographic region is bounded by a rectangle). FIG.6A shows a boundary 620 for a buffer of 50 meters around the geographicregion 610 a and a boundary 630 for buffer of 100 meters around thegeographic region 610 a. The vehicle computing system 120 switches thecurrent geographical region of a vehicle from one geographical region tothe neighboring geographical region when the vehicle crosses a thresholddistance within this buffer. For example, as shown in FIG. 6B, a vehiclestarts at location 650 a in the geographical region 610 a. The vehicletraverses along a route to reach a location 650 b where it cross theboundary of the geographical region 610 but stays within the boundary620 of the buffer. Accordingly, the vehicle computing system 120continues to use the geographical region 610 a as the currentgeographical region of the vehicle. Once the vehicle crosses theboundary 620 of the buffer at location 650 c, the vehicle computingsystem 120 switches the current geographical region of the vehicle togeographical region 610 b from 610 a. The use of a buffer prevents rapidswitching of the current geographical region of a vehicle as a result ofthe vehicle travelling along a route that closely tracks a boundary of ageographical region.

Lane Representations in HD Maps

The HD map system 100 represents lane information of streets in HD maps.Although the embodiments described herein refer to streets, thetechniques are applicable to highways, alleys, avenues, boulevards, orany other path on which vehicles can travel. The HD map system 100 useslanes as a reference frame for purposes of routing and for localizationof a vehicle. The lanes represented by the HD map system 100 includelanes that are explicitly marked, for example, white and yellow stripedlanes, lanes that are implicit, for example, on a country road with nolines or curbs but two directions of travel, and implicit paths that actas lanes, for example, the path that a turning car makes when entering alane from another lane. The HD map system 100 also stores informationrelative to lanes, for example, landmark features such as road signs andtraffic lights relative to the lanes, occupancy grids relative to thelanes for obstacle detection, and navigable spaces relative to the lanesso the vehicle can efficiently plan/react in emergencies when thevehicle must make an unplanned move out of the lane. Accordingly, the HDmap system 100 stores a representation of a network of lanes to allow avehicle to plan a legal path between a source and a destination and toadd a frame of reference for real time sensing and control of thevehicle. The HD map system 100 stores information and provides APIs thatallow a vehicle to determine the lane that the vehicle is currently in,the precise vehicle location relative to the lane geometry, and allrelevant features/data relative to the lane and adjoining and connectedlanes.

FIG. 7 illustrates lane representations in an HD map, according to anembodiment. FIG. 7 shows a vehicle 710 at a traffic intersection. The HDmap system provides the vehicle with access to the map data that isrelevant for autonomous driving of the vehicle. This includes, forexample, features 720 a and 720 b that are associated with the lane butmay not be the closest features to the vehicle. Therefore, the HD mapsystem 100 stores a lane-centric representation of data that representsthe relationship of the lane to the feature so that the vehicle canefficiently extract the features given a lane.

The HD map system 100 represents portions of the lanes as lane elements.A lane element specifies the boundaries of the lane and variousconstraints including the legal direction in which a vehicle can travelwithin the lane element, the speed with which the vehicle can drivewithin the lane element, whether the lane element is for left turn only,or right turn only, and so on. The HD map system 100 represents a laneelement as a continuous geometric portion of a single vehicle lane. TheHD map system 100 stores objects or data structures representing laneelements that comprise information representing geometric boundaries ofthe lanes; driving direction along the lane; vehicle restriction fordriving in the lane, for example, speed limit, relationships withconnecting lanes including incoming and outgoing lanes; a terminationrestriction, for example, whether the lane ends at a stop line, a yieldsign, or a speed bump; and relationships with road features that arerelevant for autonomous driving, for example, traffic light locations,road sign locations and so on.

Examples of lane elements represented by the HD map system 100 include,a piece of a right lane on a freeway, a piece of a lane on a road, aleft turn lane, the turn from a left turn lane into another lane, amerge lane from an on-ramp an exit lane on an off-ramp, and a driveway.The HD map system 100 represents a one lane road using two laneelements, one for each direction. The HD map system 100 representsmedian turn lanes that are shared similar to a one-lane road.

FIGS. 8A-B illustrates lane elements and relations between lane elementsin an HD map, according to an embodiment. FIG. 8A shows an example of aT junction in a road illustrating a lane element 810 a that is connectedto lane element 810 c via a turn lane 810 b and is connected to lane 810e via a turn lane 810 d. FIG. 8B shows an example of a Y junction in aroad showing label 810 f connected to lane 810 h directly and connectedto lane 810 i via lane 810 g. The HD map system 100 determines a routefrom a source location to a destination location as a sequence ofconnected lane elements that can be traversed to reach from the sourcelocation to the destination location.

HD Map Cache Management

FIG. 9 shows the system architecture of the HD map caching manager 295,according to an embodiment. The HD map caching manager 295 processes theHD map data and stores the HD map data for use by various modules of thevehicle computing system 120, according to one or more embodiments. TheHD map data comprises discretized portions of the HD map called maptiles. Processing the HD map data by the HD map caching manager 295 mayinvolve a variety of processes which may include, but not limited to,decompressing compressed formats of the HD map data, indexing the HD mapdata, partitioning the HD map data into subsections, loading the HD mapdata, and storing the HD map data. Additionally, the HD map cachingmanager 295 may comprise a variety of modules for accomplishing theprocessing of the HD map data and/or a variety of stores for storing theHD map data in its initial format, its intermediary formats inprocessing of the HD map data, and/or its terminal format for use by theother modules. In one or more embodiments, the HD map caching manager295 includes a map tile decompression module 920 and a map tile loadingmodule 930 for processing the HD map data. The HD map caching manager295 also includes a map tile slow cache store 940, a map tile fast cachestore 950, and a map tile RAM 960. In some embodiments, the map tileslow cache store 940 could serve similar functionality as the local HDmap store 275 of FIG. 2 . In other embodiments, the map tile RAM 960could be incorporated in another RAM on the vehicle computing system120.

The map tile decompression module 920 decompresses the compressed HD mapdata received by the online HD map system 110. The map tiledecompression module 920 performs a series of algorithms to thecompressed HD map data for restoring the resolution of the HD map data.With varying compression algorithms, the map tile decompression module920 may vary in its decompression algorithms. Some examples ofcompression models utilize probability in creating probabilistic modelsfor determining statistical patterns within the HD map data. Thestatistical patterns are reduced to statistical redundancies whichrepresent the full sized HD map data. In these examples, the map tiledecomposition module 920 receives the statistical redundancies as thecompressed HD map data and reconstructs the full sized HD map data withalgorithms applied to the statistical redundancies. After the map tiledecompression module 920 decompresses the compressed HD map data intodecompressed HD map data, the map tile decompression module 920 storesthe decompressed HD map data as accessible map tiles. In additionalembodiments, the map tile decompression module 920 indexes thedecompressed HD map data. As the HD map data is partitioned into maptiles, the map tile decompression module 920 may augment the accessiblemap tiles with coordinates of a grid. The coordinates of the grid helpin relating accessible map tiles to one another in context of the grid.In a simple representative sample, the accessible map tiles have squaredimensions; the map tile decompression module 920 augments eachaccessible map tile with a pair of coordinates (i.e., (2, 3) or (3,12)). In line with the indexing of the accessible map tiles, the maptile decompression module 920 may further augment each accessible maptile with other metadata. Different types of metadata may includecategories of map tiles, unique identifiers for each map tile,timestamps of receipt of each map tile, location data such as GPScoordinates, etc.

The map tile loading module 930 loads the accessible map tiles into themap tile RAM 960. As the accessible map tiles are in a decompressedformat, the accessible map tiles are larger sized files compared to themap tiles in the compressed format. The map tile loading module 930retrieves select accessible map tiles for loading in the map tile RAM960. The map tile loading module 930 determines which accessible maptiles to retrieve for loading in the map tile RAM 960. The map tileloading module 930 receives localization data specifying a position ofthe vehicle 150 in the HD map. The map tile loading module 930identifies the current accessible map tile in which the position of thevehicle 150 is located. The map tile loading module 930 loads thecurrent accessible map tile in the map tile RAM 960. The map tileloading module 930 can determine subsequent subsets of accessible maptiles in preparation for loading in the map tile RAM 960. Furtherdiscussion of one or more methods for determining such subsets ofaccessible map tiles will be discussed in conjunction with FIG. 12 .

The stores for storing the varying formats of the HD map data by the HDmap caching manager 295 include the map tile slow cache store 940, themap tile fast cache store 950, and the map tile RAM 960. The varyingstores streamline efficient decompression and loading of the map tilesstored by the vehicle computing system 120. In one or more embodiments,the map tile slow cache store 940 is integrated as or as part of thelocal HD map store 275. The map tile slow cache store 940 stores acompressed format of map tiles. Storing compressed map tiles in the maptile slow cache store 940 is relatively inexpensive in terms ofcomputing resources. The map tile fast cache store 950 storesdecompressed map tiles with any additional features added by thedecompression module 920 as accessible map tiles. Storing decompressedmap tiles is costly in regards to computing resources as decompressedmap tiles maintain the full resolution of the HD map as generated by theonline HD map system 110. Additional features such as indices or tagsassociated with the decompressed map tiles increase the amount of dataeach accessible map tile contains. The map tile RAM 960 storesaccessible map tiles currently in use by the vehicle computing system120. The HD map caching manager 960 provides the accessible map tileswithin the map tile RAM 960 for interaction with the other modules ofthe vehicle computing system 120 in navigating the vehicle 150.

FIG. 10 illustrates a method of efficiently utilizing compressed maptiles 1015 of the HD map from the online HD map system 110 by a vehiclecomputing system 120, according to an embodiment.

Upon determining a route, the vehicle computing system 120 may providethe HD map caching manager 295 with the determined route. The HD mapcaching manager assesses whether all map tiles corresponding to thedetermined route are present in the map tile slow cache store 940 and upto date. If there is a need for additional map tiles or updated maptiles, the HD map caching manager 295 prepares a request for map tiles1005 to be sent to the online HD map system 110 for the needed maptiles. The request for map tiles 1005 is passed through the online HDmap system interface 280 to the online HD map system 110. The online HDmap system 110 responds to the request 1005 with one or more compressedmap tiles 1015. The online HD map system interface 280 directs thecompressed map tiles 1015 to the HD map caching manager 295.

The HD map caching manager 295 receives the compressed map tiles 1015and prepares them for use by the various modules of the vehiclecomputing system 120. The HD map caching manager 295 first stores thecompressed map tiles 1015 in the map tile slow cache store 940. As themap tile slow cache store 940 stores map tiles in the compressed format,the map tile slow cache store 940 may store compressed map tiles for aplurality of routes or for an entire region of the HD map. In somecases, the HD map caching manager 295 evaluates previously usedcompressed map tiles stored in the map tile slow cache 940 for removalfrom the map tile slow cache 940. In any case, the HD map cachingmanager stores compressed map tiles in the map tile slow cache store 940as compressed map tiles are relatively inexpensive in terms of computingresources. According to the determined route, the HD map caching manager295 prompts the map tile decompression module 920 to decompress the maptiles corresponding to the determined route. The map tile decompressionmodule 920 decompresses the map tiles and adds additional features ortags such as indices. The map tile decompression module 920 stores thedecompressed map tiles as accessible map tiles 1025 in the map tile fastcache store 950. In one embodiment, the map tile fast cache store 950,at any given time, stores accessible map tiles corresponding to a singleroute. Storing decompressed map tiles with any additional featuresutilizes more computing resources than storing compressed map tiles,thus the map tile fast cache store 950 is relatively more expensive interms of computing resources compared to the map tile slow cache store940. The map tile loading module 930 receives location data of thevehicle 150 and determines a set of coordinates corresponding to thelocation of the vehicle 150 within the HD map. The map tile loadingmodule 930 retrieves the accessible map tile corresponding to the set ofcoordinates and loads the accessible map tile in the map tile RAM 960.The map tile loading 930 then retrieves subsequent accessible map tiles1025 to load in the map tile RAM 960, with the subsequent accessible maptiles 1025 selected as potential near-future locations of the vehicle150 in the HD map.

As the vehicle 150 drives along the determined route, the routegeneration module 290 dynamically updates the route. As the route isdynamically updated, the map tile loading module 930 insures that thecurrent accessible map tile is loaded in the map tile RAM 960. If not,the map tile loading module 930 loads the current accessible map tile inthe map tile RAM 960 and then loads subsequent map tiles 1025 in the maptile RAM 960 according to the dynamically updated route, wherein eachsubsequent map tiles 1025 corresponds to a dynamically updated partialroute. For example, the route generation module 290 determines likelynear-future positions of the vehicle after an amount of time (e.g., 2seconds) of driving along the current route and loads in the map tileRAM 960 the accessible map tiles corresponding to the likely near-futurepositions. The amount of time or time window can be balanced against theavailable RAM in the system, where more RAM can have a larger window oftime. In one embodiment, the map tile RAM 960, at any given time, hasaccessible map tiles currently in use by modules of the vehiclecomputing system 120 for navigating the vehicle 150 or to be used fornavigating the vehicle 150 in the near-future.

As computing memory in the map tile RAM 960 is more limited thancomputing memory in the map tile fast cache 940 and in the map tile slowcache 950, the HD map caching manager 295 can efficiently minimizeaccessible map tiles stored in the map tile RAM 960 at a given time. TheHD map caching manager 295 likewise only decompresses map tiles from themap tile slow cache 940 for determined routes, thereby minimizingdecompressed map tiles needing to be stored by the map tile fast cache940. This method of efficiently utilizing compressed map tiles 1015 ofthe HD map from the online HD map system 110 by a vehicle computingsystem 120 minimizes needless use of computing resources. The cachingmanager 295 manages disk space used (e.g., by deciding which compressedtiles are downloaded and which tiles are decompressed on disk) and RAMused (e.g., by deciding which tiles are loaded and when).

FIG. 11 illustrates an example of the method of efficiently utilizingcompressed map tiles of the HD map from the online HD map system 110 bya vehicle computing system 120, according to an embodiment. In thisexample, the online HD map system 110 contains two components of the HDmap. The HD map contains an Occupancy Map 530 (OMap) and a Landmark Map520 (LMap), respectively. The online HD map system 110 stores the OMap530 in an editable format 1110 and similarly the LMap 520 in an editableformat 1115 at the online HD map system 110. The online HD map system110 utilizes the editable formats for generating and/or updating theOMap 530 and the LMap 520. When the updated OMap 530 and the updatedLMap 520 are ready for transference to the vehicle computing system, theonline HD map system 110 compresses the OMap 530 into a tile wire format1120 and similarly compresses the LMap 520 into a tile wire format 1125.The tile wire formats are then transferred to the vehicle computingsystem 120, specifically the HD map caching manager 295 which receivesthe tile wire formats. The HD map caching manager 295 decompresses thetile wire formats into the OMap decompressed format 1130 and the LMapdecompressed format 1135. When the HD map caching manager 295 loads theOMap decompressed format 1130 and the LMap decompressed format 1135 inthe map tile RAM 960, the map tile RAM 960 contains the OMap in-RAMformat 1140 and the LMap in-RAM format 1145 for access by other modulesof the vehicle computing system 120.

As explained above since it is not possible to download the entire maponto the car, the system provides route management to determine whichroutes are most likely to be driven in the near future by the car. Whena route is selected to be driven, the system ensures that the data forthat route is cached on the car before the car begins driving the route.Once the data is download onto the car, the system makes sure thevehicle can use it efficiently, which means it is loaded into memory. Touse the data efficiently (low latency), it needs to be accessible usingsome optimized data structure, typically an indexed data structure likea map or a tree. Instead of sending indexed data from the online system,the system optimizes the network bandwidth usage by sending a veryhighly compressed form of the map data that contains only the essentialmaterial needed. This provides a relatively small payload from theonline system and a small disk footprint when stored on the car's disk.In some cases, this is called the Level 3 Disk cache, which correspondsto the tile wire formats, 1120 and 1125. This can be kept on relativelyslow and inexpensive disk.

From the Level 3 cache, the system may construct a more usable diskcache that can be loaded into memory directly and used immediately,referred to as a Level 2 cache (corresponds to the decompressed formats1130 and 1135). This cache may contain a set of files which: (1) containthe decompressed map data, (2) contain any indexes needed by the APIs toaccess the data, (3) are split up into sub-tiles to allow for access ofthe data at a granular level that is optimized for dynamic loading asthe car moves through the world, and (4) are directly loadable intomemory and provide immediate access to the data. Level 2 cache files mayonly be created when anticipated to be used, i.e., on or along a routethat is about to commence.

The memory cache can be called a Level 1 cache (corresponds to RAM, 1140and 1145). At this level, there may be possible paths of the vehicle,and the system may maintain an active wavefront around and in front ofthe vehicle of the tiles and subtiles. After each localization call, thesystem can update the estimate of the vehicle's location. The system canthen re-analyze the possible paths for the next few seconds of travel,and initiate loads of the predicted tiles and subtiles from the Level 2cache. These loads happen asynchronously while the computations arehappening, and the Level 1 cache can safely assume that the necessarydata is always loaded before an access request for the data occurs. Thisis done by ensuring that the predicted paths are sufficiently ahead ofthe car and these paths are updated frequently.

FIG. 12 illustrates a flowchart 1200 of the method of efficientlyutilizing compressed map tiles of the HD map from the online HD mapsystem 110 by a vehicle computing system 120, according to anembodiment. The method begins a passenger requesting to drive a route,and with sending 1210 information describing a route to be travelled bythe autonomous vehicle to the online HD map system 110. The system canensure that the necessary tiles are loaded onto the vehicle. Forexample, it can inspect the Level 3 cache to identify map tiles that arealready downloaded and collect the version of each. The vehicle can senda request to the online system with a list of the tiles needed as wellas the versions that the vehicle already has. The method follows withreceiving 1220 the plurality of compressed map tiles from the online HDmap system 110. For example, the online system responds with the list ofthe latest tiles that are available. The system may not return tilesthat are already up-to-date on the vehicle. The vehicle can make arequest to download each new tile. If the disk is full, the vehicle mayneed to delete Level 3 tiles that are least recently used to make room.The vehicle may now ensure that all the needed tiles have been convertedto Level 2 cache. If disk is full, the vehicle may delete Level 2 tilesthat are least recently used to make room.

The system decompresses 1230 the plurality of compressed map tiles intoaccessible map tiles. The system further determines 1240 localizationdata describing a position of the autonomous vehicle along a firstportion of the route and identifies 1250 a first accessible map tilebased in part on the localization data. The in-RAM cache can beinitialized, and a localization bootstrap is performed that usessynchronous loading of tiles around the car, which can take 1 second,for example. In one example, the system loads 1260 the first accessiblemap tile in a RAM, for utilization in driving the autonomous vehicle.The system further determines 1270 a first subset of accessible maptiles based in part on the localization data, each accessible map tilecorresponding to a second portion of the route, and then loads 1280 thefirst subset of accessible map tiles in the RAM. Then the system mayaccess 1290 the first accessible map tile from the RAM for use indriving the autonomous vehicle. In other words, once the location of thecar is determined, the in-RAM cache can use the computed route and car'sposition and heading to predict the next 2 seconds worth of possibletravel, and can load all of these tiles into RAM. Now the car mayproceed on the route. As the car collects sensor data (e.g., at 10-20Hz), it updates its estimate and the in-RAM cache re-computes thepredicted paths and asynchronously loads those tiles while removingtiles that are no longer in the predicted car area or envelope.

FIG. 13 illustrates an example of a process of loading accessible maptiles 1310 in a random-access memory (RAM) for use in driving thevehicle, according to an embodiment. The vehicle computing system 120receives a start 1320 and a destination 1330. The vehicle computingsystem 120 locates geographical coordinates of the start 1320 and thedestination 1330 as corresponding to map tiles of the plurality of maptiles 1310 within the HD map 1300. The vehicle computing system 120determines one or more routes—Route A 1340 and Route B 1350—for drivingthe autonomous vehicle from the start 1320 to the destination 1330. Asillustrated, Route A 1340 and Route B 1350 drive along the same map tileinitially but diverge to drive along different map tiles to thedestination 1330. As two routes are determined as potential routes fordriving the autonomous vehicle, the vehicle computing system 120decompresses all map tiles 1310 inclusive of Route A 1340 and Route B1350. The vehicle computing system 120 additionally decompresses maptiles neighboring map tiles along Route A 1340 and Route B 1350. Thevehicle computing system 120 loads the current map tile in arandom-access memory (RAM) of the vehicle computing system 120. As theautonomous vehicle begins to drive along one of the routes, the vehiclecomputing system 120 continuously determines localization data. Thevehicle computing system 120 may continuously determine which currentmap tile corresponds to the current location of the autonomous vehicle.Likewise, the vehicle computing system 120 loads map tiles ahead of thecurrent location of the autonomous vehicle such that the vehiclecomputing system 120 can access map tiles ahead of the current location.In some instances, the map tiles ahead of the current location helps thevehicle computing system 120 update routes.

Map Tiles

The system API provides access to: (1) 3D Occupancy Map (OMap) datawhich is a 3D volumetric grid representation of all the roads andsurroundings, (2) 3D Landmark Map (Lmap) data which is a 3Drepresentation of lanes and line and signs that represents the 3Dconstraints of the road as well as semantic rules of the road. The OMapdata is generally used for fast computation of the location of the carrelative to the map. This can be performed using various differenttechniques, including for example a technique generally known asIterative Closest Point. It takes a sensor point reading from, forexample, LiDAR sensor and computes the closest point from the LiDARpoint to a point on the map. It will generally do millions of theselookups per second.

To facilitate this fast lookup, the Level 2 cache representation of theOMap can be in a KDTree (other spatial indexes could be handledequivalently). The wire format OMap tiles can be roughly 500 m×500 m inarea, which is much bigger than the car's nominal predicted envelopeover 2 secs. Loading a KDTree with that much data is prohibitive in diskread cost and memory usage. Instead, the system can divide up the tilesinto a grid of subtiles (e.g., 8×8 subtiles). These subtiles can bestored as files in the Level 2 cache and the spatial search index can bepre-computed and stored with the subtiles. Thus, the subtiles can beloaded directly into RAM and can be instantly used for spatial lookups.In addition, indexes on other attributes can be precomputed to enabledifferent types of localization methods, for example, indexing on thecolor or intensity of points, or an attribute which identifies if thepoint is on the ground or another type of object.

To save space, the subtiles can be stored in their own coordinate systemso the coordinates can be encoded in fewer bytes. This typicallyrequires a translation of the query point and results before and aftereach KDTree lookup in a subtile. The API can access the OMap as if itwere a single KDTree, but this may actually be a KDTree wrapping a bunchof smaller adjacent KDTrees.

The Landmark Map Data can be organized as a connected graph of LaneElements. Lane Elements are the generic map entity that the car makesuse of Lane Elements can be accessed in 2 ways: (1) spatial lookup byLatLngAlt to find Lane Elements that overlap a specific location, and(2) lookup by identifier or ID. The spatial lookup can be managed byputting the Lane Elements into a Spatial 2D Array indexed by LatLng. Fora specific LatLng, the system can get a list of Lane Elements for whichthere will be a more detailed geometric overlap test. The system thenreturns the overlapping Lane Elements. Lookup by ID may simply be a mapfrom ID to Lane Element.

Landmark Map data is small enough that an entire LMap tile can be loadedinto RAM efficiently, according to some embodiments. The LMap tiles canbe kept loaded much like the OMap tiles by the in-RAM cache manager.Access can be made immediate by the API as these lookups are directlysupported in RAM.

In one embodiment, the Landmark map is a connected graph of laneelements, where the graph spans the tiles. The information in theLandmark Map, (e.g., semantics, rules, and geometry) is retrievable viathe lane element. This system allows the data to be loadable into RAM sothat accesses to the Landmark map are essentially memory lookups. Thisthus turns the more conventional process involving (1) a localizationresult returning a Lat Lng and heading, (2) determining the spatial areaof interest around the Lat Lng heading, (3) querying the database (e.g.,using SQL) to retrieve map elements of a specific type, and (4) walkingthrough a list of results to find the element(s) of interest into aprocess simply involving (1) a localization result providing a laneelement ID (e.g., answering the query “where am I”), and (2) looking atthe lane element (already in RAM) via: const LaneElement&lane_element=lane_element_array[lane_element_id] (which is implementedin an API as a function such as GetLaneElement(int id), but what itactually does is an array index of the information which is alreadyloaded into RAM).

Computing Machine Architecture

FIG. 14 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in a processor (or controller). Specifically, FIG. 14 shows adiagrammatic representation of a machine in the example form of acomputer system 1400 within which instructions 1424 (e.g., software) forcausing the machine to perform any one or more of the methodologiesdiscussed herein may be executed. In alternative embodiments, themachine operates as a standalone device or may be connected (e.g.,networked) to other machines. In a networked deployment, the machine mayoperate in the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a personal digitalassistant (PDA), a cellular telephone, a smartphone, a web appliance, anetwork router, switch or bridge, or any machine capable of executinginstructions 1424 (sequential or otherwise) that specify actions to betaken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute instructions1424 to perform any one or more of the methodologies discussed herein.

The example computer system 1400 includes a processor 1402 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), adigital signal processor (DSP), one or more application specificintegrated circuits (ASICs), one or more radio-frequency integratedcircuits (RFICs), or any combination of these), a main memory 1404, anda static memory 1406, which are configured to communicate with eachother via a bus 1408. The computer system 1400 may further includegraphics display unit 1410 (e.g., a plasma display panel (PDP), a liquidcrystal display (LCD), a projector, or a cathode ray tube (CRT)). Thecomputer system 1400 may also include alphanumeric input device 1412(e.g., a keyboard), a cursor control device 1414 (e.g., a mouse, atrackball, a joystick, a motion sensor, or other pointing instrument), astorage unit 1416, a signal generation device 1418 (e.g., a speaker),and a network interface device 1420, which also are configured tocommunicate via the bus 1408.

The storage unit 1416 includes a machine-readable medium 1422 on whichis stored instructions 1424 (e.g., software) embodying any one or moreof the methodologies or functions described herein. The instructions1424 (e.g., software) may also reside, completely or at least partially,within the main memory 1404 or within the processor 1402 (e.g., within aprocessor's cache memory) during execution thereof by the computersystem 1400, the main memory 1404 and the processor 1402 alsoconstituting machine-readable media. The instructions 1424 (e.g.,software) may be transmitted or received over a network 1426 via thenetwork interface device 1420.

While machine-readable medium 1422 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions (e.g., instructions 1424). The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring instructions (e.g., instructions 1424) for execution by themachine and that cause the machine to perform any one or more of themethodologies disclosed herein. The term “machine-readable medium”includes, but not be limited to, data repositories in the form ofsolid-state memories, optical media, and magnetic media.

Additional Configuration Considerations

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

For example, although the techniques described herein are applied toautonomous vehicles, the techniques can also be applied to otherapplications, for example, for displaying HD maps for vehicles withdrivers, for displaying HD maps on displays of client devices such asmobile phones, laptops, tablets, or any computing device with a displayscreen. Techniques displayed herein can also be applied for displayingmaps for purposes of computer simulation, for example, in computergames, and so on.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a tangible computer readable storage medium or any typeof media suitable for storing electronic instructions, and coupled to acomputer system bus. Furthermore, any computing systems referred to inthe specification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

Embodiments of the invention may also relate to a computer data signalembodied in a carrier wave, where the computer data signal includes anyembodiment of a computer program product or other data combinationdescribed herein. The computer data signal is a product that ispresented in a tangible medium or carrier wave and modulated orotherwise encoded in the carrier wave, which is tangible, andtransmitted according to any suitable transmission method.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon.

What is claimed is:
 1. A method comprising: storing a plurality of maptiles in a compressed format; loading, based at least on a determinedroute for a machine, a first subset of map tiles from the plurality ofmap tiles into a storage cache in a decompressed format; loading, basedat least on a predicted movement corresponding to the machine, a secondsubset of map tiles from the first subset of map tiles from the storagecache into random access memory (RAM); and performing one or morenavigation operations by the machine based at least on the second subsetof map tiles stored in the RAM.
 2. The method of claim 1, wherein thepredicted movement is within a threshold time interval from a point intime at which the machine is at a determined location.
 3. The method ofclaim 2, wherein the determined location is based at least on one ormore of: sensor data obtained using one or more sensors corresponding tothe machine; or one or more map tiles previously loaded to the RAM. 4.The method of claim 1, wherein the predicted movement is based at leaston a plurality of previous locations corresponding to the machine over aparticular amount of time.
 5. The method of claim 1, wherein at leastone map tile of the second subset of map tiles corresponds to an areathat is adjacent to a current area corresponding to a current map tilethat includes a determined location of the machine.
 6. A processorcomprising: processing circuitry to cause performance of operationscomprising: storing a plurality of map tiles in a compressed format infirst storage location; loading a first subset of map tiles from theplurality of map tiles from the first storage location to a secondstorage location in a decompressed format; loading a second subset ofmap tiles of the first subset of map tiles from the second storagelocation to a third storage location based at least on the second subsetof map tiles corresponding to a predicted movement corresponding to amachine, the third storage location corresponding to a temporary memorystorage; and performing one or more operations by the machine based atleast on the second subset of map tiles stored in the third storagelocation.
 7. The processor of claim 6, wherein the first subset of maptiles are selected for storage in the second storage location based atleast on the first subset of map tiles corresponding to a determinedroute corresponding to the machine.
 8. The processor of claim 6, whereinthe predicted movement is determined based at least on a determinedlocation corresponding to the machine.
 9. The processor of claim 8,wherein the determined location is determined based at least on one ormore of: sensor data obtained using one or more sensors corresponding tothe machine; or one or more map tiles previously loaded to the thirdstorage location.
 10. The processor of claim 6, wherein the predictedmovement is within a threshold time interval from a point in time atwhich the machine is at a location.
 11. The processor of claim 6,wherein the predicted movement is based at least on localization datacorresponding to the machine and corresponding to a plurality of pointsin time.
 12. The processor of claim 6, wherein the third storagelocation has faster accessibility than the second storage location, andthe second storage location has faster accessibility than the firststorage location.
 13. The processor of claim 6, wherein the thirdstorage location comprises random access memory (RAM).
 14. A systemcomprising: one or more processing units to cause performance ofoperations comprising: loading, based at least on a determined route ofa machine, a first subset of map tiles stored in a compressed formatinto a first temporary storage location in a decompressed format;loading, based at least on predicted movement of the machine from acurrent location to a determined location along a portion of thedetermined route, a second subset of map tiles from the first subset ofmap tiles to a second temporary storage location; and accessing thesecond subset of map tiles during navigation of the machine along atleast the portion of the determined route.
 15. The system of claim 14,wherein the first subset of map tiles and the second subset of map tilesare included a set of map tiles corresponding to a high definition (HD)map.
 16. The system of claim 14, wherein the second temporary storagelocation is associated with faster access than the first temporarystorage location.
 17. The system of claim 14, wherein at least one tilein the first subset of map tiles is associated with one or morelocations further from a current location of the machine than the secondsubset of map tiles.
 18. The system of claim 14, wherein the predictedmovement is within a threshold time interval from a point in time atwhich the machine is at the current location.
 19. The system of claim14, wherein the first temporary storage includes a cache and the secondtemporary storage includes random access memory (RAM).
 20. The system ofclaim 14, wherein the first subset of tiles and the second subset oftiles are stored in a compressed format prior to being loaded into thefirst temporary storage location in the decompressed format.